我正在尝试修改由软件开发人员（Kyle Dickerson）编写的代码，并将其编写为：
所以我有这段代码：
from __future__ import division
## Kyle Dickerson
## kyle.dickerson@gmail.com
## Jan 15, 2008
##
## Self-organizing map using scipy
## This code is licensed and released under the GNU GPL
## This code uses a square grid rather than hexagonal grid, as scipy allows for fast square grid computation.
## I designed sompy for speed, so attempting to read the code may not be very intuitive.
## If you're trying to learn how SOMs work, I would suggest starting with Paras Chopras SOMPython code:
## http://www.paraschopra.com/sourcecode/SOM/index.php
## It has a more intuitive structure for those unfamiliar with scipy, however it is much slower.
## If you do use this code for something, please let me know, I'd like to know if has been useful to anyone.
from random import *
from math import *
import sys
import scipy
import numpy
class SOM:
def __init__(self, height=4, width=4, FV_size=3, learning_rate=0.005):
self.height = height
self.width = width
self.FV_size = FV_size
self.radius = (height+width)/3
self.learning_rate = learning_rate
self.nodes = scipy.array([[ [random()*255 for
i in range(FV_size)] for x in range(width)] for y in range(height)])
self.nodes = scipy.array([[1,2,3],[4,5,6],[4,5,6],
[4,5,6],[4,5,6], [4,5,6],[4,5,6],[4,5,6],[4,5,6],
[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6]])
print "SOM",self.nodes
def train(self, iterations=1000, train_vector=[[]]):
for t in range(len(train_vector)):
train_vector[t] = scipy.array(train_vector[t])
print "training",train_vector[t],t
time_constant = iterations/log(self.radius)
delta_nodes = scipy.array([[[0 for i in range(self.FV_size)]
for x in range(self.width)] for y in range(self.height)])
for i in range(1, iterations+1):
delta_nodes.fill(0)
radius_decaying=self.radius*exp(-1.0*i/time_constant)
rad_div_val = 2 * radius_decaying * i
learning_rate_decaying=self.learning_rate*exp(-1.0*i/time_constant)
sys.stdout.write("\rTraining Iteration:
" + str(i) + "/" + str(iterations))
for j in range(len(train_vector)):
best = self.best_match(train_vector[j])
for loc in self.find_neighborhood(best, radius_decaying):
influence = exp( (-1.0 * (loc[2]**2)) / rad_div_val)
inf_lrd = influence*learning_rate_decaying
delta_nodes[loc[0],loc[1]] += inf_lrd*
(train_vector[j]- self.nodes[loc[0],loc[1]])
self.nodes += delta_nodes
sys.stdout.write("\n")
# Returns a list of points which live within 'dist' of 'pt'
# Uses the Chessboard distance
# pt is (row, column)
def find_neighborhood(self, pt, dist):
min_y = max(int(pt[0] - dist), 0)
max_y = min(int(pt[0] + dist), self.height)
min_x = max(int(pt[1] - dist), 0)
max_x = min(int(pt[1] + dist), self.width)
neighbors = []
for y in range(min_y, max_y):
for x in range(min_x, max_x):
dist = abs(y-pt[0]) + abs(x-pt[1])
neighbors.append((y,x,dist))
return neighbors
# Returns location of best match, uses Euclidean distance
# target_FV is a scipy array
def best_match(self, target_FV):
loc = scipy.argmin((((self.nodes - target_FV)**2).sum(axis=2))**0.5)
r = 0
while loc > self.width:
loc -= self.width
r += 1
c = loc
return (r, c)
# returns the Euclidean distance between two Feature Vectors
# FV_1, FV_2 are scipy arrays
def FV_distance(self, FV_1, FV_2):
return (sum((FV_1 - FV_2)**2))**0.5
if __name__ == "__main__":
print "Initialization..."
colors = [ [0, 0, 0], [0, 0, 255], [0, 255, 0],
[0, 255, 255], [255, 0, 0], [255, 0, 255],
[255, 255, 0], [255, 255, 255]]
width = 32
height = 32
color_som = SOM(width,height,3,0.05)
print "Training colors..."
color_som.train(1000, colors)
try:
from PIL import Image
print "Saving Image: sompy_test_colors.png..."
img = Image.new("RGB", (width, height))
for r in range(height):
for c in range(width):
img.putpixel((c,r),(int(color_som.nodes[r,c,0]),
int(color_som.nodes[r,c,1]), int(color_som.nodes[r,c,2])))
print "color nodes",color_som.nodes
img = img.resize((width*10, height*10),Image.NEAREST)
img.save("sompy_test_colors.png")
except:
print "Error saving the image, do you have PIL (Python Imaging Library) installed?"
但是当我试图从
开始self.nodes = scipy.array([[ [random()*255
for i in range(FV_size)] for x in range(width)]
for y in range(height)])
在原始代码中是这样的：
self.nodes = scipy.array([[1,2,3],[4,5,6],[4,5,6],
[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],
[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6]])
我收到错误消息：
File "sompy5.py", line 112, in <module>
color_som.train(1000, colors)
File "sompy5.py", line 65, in train
best = self.best_match(train_vector[j])
File "sompy5.py", line 92, in best_match
loc = scipy.argmin((((self.nodes - target_FV)**2).sum(axis=2))**0.5)
File "/usr/lib/python2.7/dist-packages/numpy/core/_methods.py",
line 25, in _sum
out=out, keepdims=keepdims)
ValueError: 'axis' entry is out of bounds
是否需要做一些事情才能让矢量匹配？
答案 0 :(得分：0)
这部分是一个三维数组（开始参数的3个方括号）：
self.nodes = scipy.array([[ [random()*255 for
i in range(FV_size)] for x in range(width)] for y in range(height)])
这部分是一个二维数组：
self.nodes = scipy.array([[1,2,3],[4,5,6],[4,5,6],
[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],
[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6],[4,5,6]])
因此，您需要将self.nodes转换为适当的3-D数组。
编辑：所需语法的示例：
self.nodes = scipy.array([[ [1,2,3],[4,5,6]] , [[7,8,9],[10,11,12]]])
print(self.nodes)
>>> array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
编辑2：
另一个选择是构建线性数组，然后重塑形状（）：
myarray = scipy.array([1,2,3,4,5,6,7,8,9,10,11,12])
myarray = myarray.reshape( (2, 2, 3) ) ## 3 numbers for 3 dimensions, but the product must be the same as the number of elements of the original array
print(myarray)
>>> array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])